Agent skill

Wf Spec Drive

by changkun in changkun/wallfacer

Run the whole lifecycle for one spec, calling the other skills in order and advancing one legal transition at a time until it reaches a target state (default complete), stopping to ask at…

MITAuto-check passedDevelopment

Install Wf Spec Drive

skills CLI
$ npx skills add changkun/wallfacer --skill wf-spec-drive -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install changkun/wallfacer wf-spec-drive --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/changkun/wallfacer.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/wf-spec-drive .claude/skills/wf-spec-drive && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
wf-spec-drive
GitHub stars
112
Token cost
~2.3k tokens
SKILL.md length
1,165 words
Files
1
Skills in repo
14
Repo updated
First seen
Licence
MIT

At a glance

Run the whole lifecycle for one spec, calling the other skills in order and advancing one legal transition at a time until it reaches a target state (default complete), stopping to ask at…

  • Works in 4 steps: Read reality → Pick the next step (decision table) → Gate check before acting → …
  • Development work in your project
  • SKILL.md covers The canonical lifecycle, How status changes (hybrid), Step 1: Read reality and Step 2: Pick the next step…, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Wf Spec Drive is an agent skill from changkun/wallfacer. Run the whole lifecycle for one spec, calling the other skills in order and advancing one legal transition at a time until it reaches a target state (default complete), stopping to ask at irreversible gates. Use when the user wants a spec taken from wherever it is to done rather than running each step by hand — and start here when unsure which single-spec skill applies.

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development. The repository describes itself as: Chat, specs, tasks, and code. An autonomous engineering platform. Full autonomy when you trust it. Full control when you don't. The licence is MIT.

When your agent uses it

  • Development work in your project

Example prompts

  • “/wf-spec-drive”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Read reality
  2. Pick the next step (decision table)
  3. Gate check before acting
  4. Execute, then report

What it can do on your machine

Read from SKILL.md and the folder at commit 9fc9f06. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Wf Spec Drive loads about 2.3k tokens when it runs. Until then it costs about 97 tokens; SKILL.md has 1,165 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~97
When it runs · the whole SKILL.md, loaded when a task matches
~2.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from changkun/wallfacer at commit 9fc9f06, republished under its MIT licence (© changkun). 1,165 words, ~2,286 tokens.

Download SKILL.mdSave it as .claude/skills/wf-spec-drive/SKILL.md (or your agent's skills folder).
name
wf-spec-drive
description
Run the whole lifecycle for one spec, calling the other skills in order and advancing one legal transition at a time until it reaches a target state (default complete), stopping to ask at irreversible gates. Use when the user wants a spec taken from wherever it is to done rather than running each step by hand — and start here when unsure which single-spec skill applies.
argument-hint
<spec-file> [target-status=complete]
user-invocable
true

Drive a Spec Through the Lifecycle

Advance the spec at the first argument toward a target state (default complete), one or more legal transitions at a time, reporting the new state at the end of every turn. This is the orchestrator: it reads reality, picks the next step, runs the right /wf-spec-* sub-skill (or transition API call), and stops at gates.

It is built to be re-invoked: under a /goal the Stop-hook evaluator re-runs you each turn with the remaining work, so each turn must (a) make concrete progress and (b) end by stating the spec's current status in your message, so the evaluator can judge whether the goal is met. Within a turn, carry the spec through every legal, non-gated transition you can; end the turn at a gate, at the target, or when the next step waits on work outside this session.

The canonical lifecycle

Seven states. status is the single source of truth; transitions must follow the legal edges below — there is no implemented/in_progress state, and validated → complete is ILLEGAL. A spec reaches complete only through testing, where a drift verdict is rendered.

vague      → drafted | archived
drafted    → validated | stale | archived
validated  → testing | stale
testing    → complete | stale | archived
complete   → stale | archived
stale      → drafted | validated | archived
archived   → drafted            (resurrection)

Server-automatic vs. agent-initiated:

EdgeWho drives it
drafted → validatedagent (validate action) or folder-dispatch auto-promote
validated → testingserver on task-done (drift pipeline); or wrap-up for the direct path
testing → complete / staleserver drift verdict; or force-complete; or wrap-up's verdict
any → stale (fan-out)server on task-done / chat-edit; agent may stale manually
any → archived, archived → draftedagent (archive / unarchive)

How status changes (hybrid)

By default there is no server: advance the status by editing the spec's frontmatter along a legal edge and committing it, exactly as the other skills do.

Where a transition API is present, prefer it because it is authoritative, validates the edge, runs drift / stale fan-out, and commits: POST /api/specs/transition with { "action": "<action>", "path": "<workspace-relative spec path>" }. Actions: dispatch, undispatch, archive, unarchive, validate, stale, unstale, dismiss-stale, force-complete, migrate. /wf-spec-dispatch uses the same endpoint.

Whether you edit frontmatter or call the API, only ever move along a legal edge above. Never write an illegal jump (e.g. validated → complete).

Note there is no API action to enter testing or to complete from validated directly; the server enters testing on task-done, and force-complete only does testing → complete. The direct-implement path (below) therefore walks validated → testing → complete itself.

Step 1: Read reality

  1. Read the spec's frontmatter: status, dispatched_task_id, depends_on, affects, and whether it has a child-spec directory (non-leaf).
  2. If dispatched_task_id is set, check the linked task's status (done / in_progress / failed) on the board, where one exists.
  3. Establish the target (arg 2, default complete) and confirm the spec is not already there or past it.

Step 2: Pick the next step (decision table)

Match the current status and choose the next action toward the target:

CurrentConditionNext step
vague—/wf-spec-refine (or /wf-spec-create follow-up) to make it concrete → drafted. Then re-loop.
draftedlarge / many open questions/wf-spec-breakdown (design) → child specs; then drive the lead child. Gate? No.
draftedsmall, items clear/wf-spec-validate (lint), then validate action → validated.
validatedleaf, build in one pass/wf-spec-implement (direct, autonomous mode — no plan-mode pause under a goal), which finishes via /wf-spec-wrapup (testing→complete).
validatedwants board executionGATE: dispatching to the board is outward — confirm with the user, then /wf-spec-dispatch.
validatednon-leaf with task childrendispatch/implement each leaf (drive children); parent completes when leaves do.
testingtask done, server idle/wf-spec-wrapup renders the verdict → complete or stale.
testingtester failed (testing_pending)report; offer force-complete (a gate — confirm).
complete—At target. If the user wanted downstream specs driven, pick the next unblocked dependent.
stale—/wf-spec-refine → drafted/validated, then re-loop.
archived—Stop unless the user asked to resurrect (unarchive → drafted).

Dependencies: before implementing/dispatching, confirm every depends_on is complete (per the sub-skills' own gates). If a dependency is not complete and the target requires it, drive the dependency first or report the block.

Step 3: Gate check before acting

Pause and ask the user (do not execute) when the next step is:

  • Dispatch to the board (creates outward work / a running task).
  • Archive (retires the spec + descendants; relocates files).
  • Stale fan-out across a dependency tree (marks other people's specs stale) — manual stale on a spec with dependents.
  • force-complete (overrides the drift gate).

For all other steps (refine, validate, breakdown, implement-direct, wrap-up of a spec you implemented this run), proceed without pausing.

Show full SKILL.md (448 more words)Show less

Step 4: Execute, then report

Run the chosen sub-skill / API call. Make all the non-gated, legal progress you can in this turn (e.g. validate → implement → wrap-up is one turn for a small leaf); stop at the first gate, the target, or a step that waits on work outside this session, such as a dispatched board task.

End every turn with a status line the goal evaluator can read, e.g.:

Spec <path>: status <old> → <new>. Target: <target>. Next: <next step | GATE: <what> | DONE>.

Running under a /goal (what is and isn't truly hands-off)

A user sets, e.g., /goal spec <path> reaches status complete. Each turn the Stop-hook evaluator reads your transcript; if the spec is not yet at the target it re-invokes you with what remains. You don't manage the loop: you make and report progress each turn. The harness pauses the goal when the evaluator keeps finding it unmet, and clears it when met.

Be honest about the reach of an unattended loop (no human watching):

  • Fully autonomous — the non-gated, in-session transitions: refine, validate, breakdown, implement (autonomous mode, no plan-mode pause), and wrap-up through the testing gate. A small validated leaf can therefore go all the way to complete unattended.
  • Stalls and needs a human — every gate in Step 3 (dispatch to the board, archive, force-complete, stale fan-out). The evaluator can't approve them and can't run commands, so an unattended loop cannot pass them, and repeated unmet checks pause the goal. When you reach a gate, state it plainly and end the turn: report "GATE: <what> (needs you)", so when the user returns they can unblock with one message.
  • Completes outside the loop — a dispatched spec finishes asynchronously on the task board; the drift pipeline (server) renders its verdict on task-done. The goal loop can't wait that out, and the evaluator can't observe the task finishing. So a goal targeting a spec you dispatch will pause at the dispatch gate; after the board task is done, re-run /wf-spec-drive (or /wf-spec-wrapup) to pick up testing → complete.

Net: /goal + /wf-spec-drive runs the in-session path to complete hands-off, and turns every outward/async step into a clearly-reported stop rather than a silent hang. Don't claim more autonomy than that.

Guidelines

  • One source of truth — never invent a status; only ever move along the legal edges above. When unsure a transition is legal, prefer the server API (it rejects illegal edges) over a YAML edit.
  • Don't double-manage dispatched specs — once a spec is dispatched, the server drives validated → testing → complete/stale on task-done. Don't hand-set its status; let the server, then run /wf-spec-wrapup only to enrich the Outcome.
  • End every turn with the status line — the closing status line is the contract with the goal evaluator; keep it accurate.
  • Respect the gates — autonomy stops at outward/irreversible actions.

© changkun, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/wf-spec-drive of changkun/wallfacer.

Open the folder on GitHubat commit 9fc9f06

Compare with similar skills

Wf Spec Drive next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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PR Babysitteropeninterpreter/openinterpreter69k3 repos~4.2kAutomated safety check: PassApache-2.0
Code Review ChecklistshareAI-lab/learn-claude-code78k4 repos~1.1kAutomated safety check: PassMIT

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Categories

Questions about Wf Spec Drive

What does Wf Spec Drive do?

Run the whole lifecycle for one spec, calling the other skills in order and advancing one legal transition at a time until it reaches a target state (default complete), stopping to ask at…. Wf Spec Drive is an agent skill from changkun/wallfacer. Run the whole lifecycle for one spec, calling the other skills in order and advancing one legal transition at a time until it reaches a target state (default complete), stopping to ask at irreversible gates.

When should I use Wf Spec Drive?

Wf Spec Drive fits situations like: development work in your project.

How do I install Wf Spec Drive in Claude Code?

Run `npx skills add changkun/wallfacer --skill wf-spec-drive -a claude-code`. Or copy the skill folder (.claude/skills/wf-spec-drive in changkun/wallfacer) into .claude/skills/wf-spec-drive in your project. Claude Code loads it when a task matches its description.

How do I install Wf Spec Drive in Codex?

Run `npx skills add changkun/wallfacer --skill wf-spec-drive -a codex`. Or copy the skill folder (.claude/skills/wf-spec-drive in changkun/wallfacer) into .agents/skills/wf-spec-drive in your project. Codex loads it when a task matches its description.

Can I use Wf Spec Drive in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add changkun/wallfacer --skill wf-spec-drive -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/wf-spec-drive, .gemini/skills/wf-spec-drive, .github/skills/wf-spec-drive and .opencode/skills/wf-spec-drive in your project.

What does Wf Spec Drive need to run?

SKILL.md names no scripts, command-line tools or credentials: Wf Spec Drive is instructions for the agent only.

Does Wf Spec Drive access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Wf Spec Drive safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Wf Spec Drive use?

Wf Spec Drive is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Wf Spec Drive use?

About 2.3k tokens (SKILL.md is roughly 9.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Wf Spec Drive?

Skills that share tags, products or a category with Wf Spec Drive: Vercel Composition Patterns (supabase/supabase, 111k stars), Finishing a Development Branch (obra/superpowers, 297k stars), Typescript Advanced Types (rolling-scopes/rsschool-app, 10k stars) and PR Babysitter (openinterpreter/openinterpreter, 69k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Wf Spec Drive?

changkun (a GitHub user) maintains it in changkun/wallfacer, which has 112 GitHub stars. The repository holds 14 skills in this directory. The repository was last updated on October 9, 2026.

Source: changkun/wallfacer on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.